  Citation: Garrido, M.C.; Cadenas, J.M.; Bueno-Crespo, A.; Martínez-España, R.; Giménez, J.G.; Cecilia, J.M. Evaporation Forecasting through Interpretable Data Analysis Techniques. Electronics 2022, 11, 536. https://doi.org/ 10.3390/electronics11040536 Academic Editors: Prasan Kumar Sahoo and Amir Mosavi Received: 23 December 2021 Accepted: 8 February 2022 Published: 10 February 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). electronics Article Evaporation Forecasting through Interpretable Data Analysis Techniques M. Carmen Garrido 1 , José M. Cadenas 1 , Andrés Bueno-Crespo 2 , Raquel Martínez-España 1,* , José G. Giménez 2 and José M. Cecilia 3 1 Department of Information and Communication Engineering, University of Murcia, 30100 Murcia, Spain; carmengarrido@um.es (M.C.G.); jcadenas@um.es (J.M.C.) 2 Department of Computer Science, Universidad Católica de Murcia, Murcia, 30107 Murcia, Spain; abueno@ucam.edu (A.B.-C.); jggimenez@ucam.edu (J.G.G.) 3 Department of Computer Engineering (DISCA), Universitat Politècnica de València, 46022 Valencia, Spain; jmcecilia@disca.upv.es * Correspondence: raquel.m.e@um.es Abstract: Climate change is increasing temperatures and causing periods of water scarcity in arid and semi-arid climates. The agricultural sector is one of the most affected by these changes, having to optimise scarce water resources. An important phenomenon within the water cycle is the evaporation from water reservoirs, which implies a considerable amount of water lost during warmer periods of the year. Indeed, evaporation rate forecasting can help farmers grow crops more sustainably by managing water resources more efficiently in the context of precision agriculture. In this work, we expose an interpretable machine learning approach, based on a multivariate decision tree, to forecast the evaporation rate on a daily basis using data from an Internet of Things (IoT) infrastructure, which is deployed on a real irrigated plot located in Murcia (southeastern Spain). The climate data collected feed the models that provide a forecast of evaporation and a summary of the parameters involved in this process. Finally, the results of the interpretable presented model are validated with the best literature models for evaporation rate prediction, i.e., Artificial Neural Networks, obtaining results very similar to those obtained for them, reaching up to 0.85R 2 and 0.6 MAE. Therefore, in this work, a double objective is faced: to maintain the performance obtained by the models most frequently used in the problem while maintaining the interpretability of the knowledge captured in it, which allows better understanding the problem and carrying out appropriate actions. Keywords: smart agriculture; evaporation forecast; interpretable machine learning; IoT 1. Introduction Access to water is a fundamental right of today’s societies. It is a vital resource for living beings but also for the economic performance, growth, and viability of many business sectors [1]. However, it is also a finite and shared resource, whose indiscriminate consumption, whether by individuals, companies, or economic sectors, can have dramatic consequences for the common good. Therefore, optimising the use of water resources is a determining factor for the social and economic stability of modern societies [2]. The application of new technologies in these sectors, such as agriculture, guides the revolution of a society with an active role to face the related water scarcity problems [3]. Irrigated agriculture accounts for 20% of total cultivated land and contributes 40% of the total food produced in the world [4]. Fortunately, the agricultural sector is increasingly applying new technologies that improve its services and processes to increase profits, reduce costs, and make the system more sustainable [5]. Precision agriculture promotes the deployments of new technologies such as IoT or Artificial Intelligence in the sector of agriculture. This discipline covers issues ranging from pest detection to water saving, frost risk management, harvesting, and climate control of greenhouses, among others. Electronics 2022, 11, 536. https://doi.org/10.3390/electronics11040536 https://www.mdpi.com/journal/electronics